Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| К-най-близки съседи× | Регресия Ласо× | Методът на опорните вектори (класификация)× | |
|---|---|---|---|
| Област | Машинно обучение | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Година на възникване≠ | 1967 | 1996 | 1995 |
| Създател≠ | Cover, T.M. & Hart, P.E. | Tibshirani, R. | Cortes, C. & Vapnik, V. |
| Тип≠ | Instance-based (non-parametric) learning | Regularized linear regression (L1 penalty) | Maximum-margin classifier (kernel method) |
| Основополагащ източник≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Други названия | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Свързани≠ | 5 | 4 | 5 |
| Резюме≠ | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateНабор от данни ↗ |
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